def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
B, T, N = emissions.size()
hypos = []
def make_hypo(result: DecodeResult) -> Dict[str, Any]:
hypo = {
"tokens": self.get_tokens(result.tokens),
"score": result.score,
}
if self.lexicon:
hypo["words"] = [
self.idx_to_wrd[x] if self.unitlm else self.word_dict[x]
for x in result.words if x >= 0
]
return hypo
for b in range(B):
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
nbest_results = results[:self.nbest]
hypos.append([make_hypo(result) for result in nbest_results])
self.lm.empty_cache()
return hypos
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
B, T, N = emissions.size()
hypos = []
for b in range(B):
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
nbest_results = results[:self.nbest]
hypos.append([{
"tokens":
self.get_tokens(result.tokens),
"score":
result.score,
"words":
[self.word_dict.get_entry(x) for x in result.words if x >= 0],
} for result in nbest_results])
return hypos
